AU2021202991B2 - Method and system for vehicle speed profile generation - Google Patents
Method and system for vehicle speed profile generation Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3484—Personalized, e.g. from learned user behaviour or user-defined profiles
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096716—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information does not generate an automatic action on the vehicle control
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0062—Adapting control system settings
- B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2300/00—Indexing codes relating to the type of vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/35—Road bumpiness, e.g. pavement or potholes
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
- B60W2555/20—Ambient conditions, e.g. wind or rain
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/10—Historical data
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2720/00—Output or target parameters relating to overall vehicle dynamics
- B60W2720/10—Longitudinal speed
- B60W2720/103—Speed profile
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2720/00—Output or target parameters relating to overall vehicle dynamics
- B60W2720/10—Longitudinal speed
- B60W2720/106—Longitudinal acceleration
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
Abstract
A method and system is provided for vehicle speed profile generation. The method is
performed by receiving data pertaining to driver characteristics and characteristics of trips
taken by said driver, creating driver profile by generating skill and aggression parameters for
said driver, constructing trip parameters pertaining to said trips taken by the driver by
processing the skill and aggression parameters, constructing acceleration dataset for said trips,
constructing speed values from the acceleration dataset and processing the speed values for
anomalies.
[To be published with FIG. 2]
2/3
200
202
Data reception module
204
Driver profile generator module
206
Trip identifier module
208
Acceleration generator module
210
Speed generator module
212
Validation module
Figure 2
Description
2/3
200
202
Data reception module
204
Driver profile generator module
206
Trip identifier module
208
Acceleration generator module
210
Speed generator module
212
Validation module
Figure 2
[0001] This patent application claims priority to Indian Specification (Title: Method
and System for vehicle speed profile generation) No. 201621024234, filed in India on July 15,
2016.
[0002] The embodiments herein generally relate to synthetic data generation, and
more particularly, to a method and system for vehicle speed profile generation.
[0003] Currently there are different analytics solutions to perform diagnostics on
acquired data from vehicles. Also such systems can record and playback and offer data
logging with real-time signal views.
[0004] Prior art illustrates an intuitive and user friendly introduction to vehicle
dynamics simulation and virtual test driving personal computers. But such solutions are
mostly purely analytics platform, not made for simulation of the acquired data and synthetic
data generation from as a result of such simulation.
[0005] On the other hand vehicle model based simulations are used by car
manufacturers for testing vehicle model concept, but these models are not suitable to generate
large scale vehicle data. Thus a system or model which offers large scale data generation of
vehicle data is not targeted as much as it should and existing solutions are not robust and
specific. Thereby, generating synthetic data in the form of a vehicle's speed profile from the acquired data of vehicles is still considered to be one of the biggest challenges of the technical domain.
[0006] Before the present methods, systems, and hardware enablement are described,
it is to be understood that this invention is not limited to the particular systems, and
methodologies described, as there can be multiple possible embodiments of the present
invention which are not expressly illustrated in the present disclosure. It is also to be
understood that the terminology used in the description is for the purpose of describing the
particular versions or embodiments only, and is not intended to limit the scope of the present
invention which will be limited only by the appended claims.
[0007] The present disclosure envisages a method and system which can generate a
vehicle's speed profile.
[0008] In an embodiment of the invention, a method for vehicle speed profile
generation is provided. The method comprises processor implemented steps of receiving data
pertaining to driver characteristics and characteristics of trips taken by said driver, creating
driver profile by generating skill and aggression parameters for said driver, constructing trip
parameters pertaining to said trips taken by the driver by processing the skill and aggression
parameters, constructing acceleration dataset for said trips, constructing speed values from the
acceleration dataset and processing the speed values for anomalies.
[0009] In another embodiment of the invention, a system for vehicle speed profile
generation is provided. The system comprises of a processor, a data bus coupled to the
processor and a computer-usable medium embodying computer code, wherein the computer
usable medium is coupled to the data bus and the computer program code comprising
instructions executable by said processor and configured for operating a data reception module (202) adapted for receiving data pertaining to driver characteristics and characteristics of trips taken by said driver, a driver profile generator module (204) adapted for creating driver profile by generating skill and aggression parameters for said driver, a trip identifier module (206) adapted for constructing parameters pertaining to said trips taken by the driver by processing the skill and aggression parameters, an acceleration generator (208) module adapted for constructing acceleration data for said trips, a speed generator module (210) adapted for obtaining speed values from the acceleration data and a validation module (212) adapted for processing the speed values for anomalies.
[0010] The embodiments herein will be better understood from the following detailed
description with reference to the drawings, in which:
[0011] Figure 1 shows a flow chart illustrating method for vehicle speed profile
generation;
[0012] Fig. 2 shows a block diagram of a system for vehicle speed profile generation;
and
[0013] Fig. 3 shows the format of the resulting data stored in a structured database and
presented to a user.
[0014] Some embodiments of this invention, illustrating all its features, will now be
discussed in detail.
[0015] The words "comprising," "having," "containing," and "including," and other
forms thereof, are intended to be equivalent in meaning and be open ended in that an item or
items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items.
[0016] It must also be noted that as used herein and in the appended claims, the
singular forms "a," "an," and "the" include plural references unless the context clearly dictates
otherwise. Although any systems and methods similar or equivalent to those described herein
can be used in the practice or testing of embodiments of the present invention, the preferred,
systems and methods are now described. In the following description for the purpose of
explanation and understanding reference has been made to numerous embodiments for which
the intent is not to limit the scope of the invention.
[0017] One or more components of the invention are described as module for the
understanding of the specification. For example, a module may include self-contained
component in a hardware circuit comprising of logical gate, semiconductor device, integrated
circuits or any other discrete component. The module may also be a part of any software
programme executed by any hardware entity for example processor. The implementation of
module as a software programme may include a set of logical instructions to be executed by a
processor or any other hardware entity.
[0018] The disclosed embodiments are merely exemplary of the invention, which may
be embodied in various forms.
[0019] The elements illustrated in the Figures interoperate as explained in more detail
below. Before setting forth the detailed explanation, however, it is noted that all of the
discussion below, regardless of the particular implementation being described, is exemplary in
nature, rather than limiting. For example, although selected aspects, features, or components
of the implementations are depicted as being stored in memories, all or part of the systems
and methods consistent with the natural disaster prediction system and method may be stored
on, distributed across, or read from other machine-readable media.
[0020] Method steps of the invention may be performed by one or more computer processors executing a program tangibly embodied on a computer-readable medium to perform functions of the invention by operating on input and generating output. Suitable processors include, by way of example, both general and special purpose microprocessors.
Generally, the processor receives (reads) instructions and data from a memory (such as a read
only memory and/or a random access memory) and writes (stores) instructions and data to the
memory. Storage devices suitable for tangibly embodying computer program instructions and
data include, for example, all forms of non-volatile memory, such as semiconductor memory
devices, including EPROM, EEPROM, and flash memory devices; magnetic disks such as
internal hard disks and removable disks; magneto-optical disks; and CD-ROMs. Any of the
foregoing may be supplemented by, or incorporated in, specially-designed ASICs
(application-specific integrated circuits) or FPGAs (Field-Programmable Gate Arrays). A
computer can generally also receive (read) programs and data from, and write (store)
programs and data to, a non-transitory computer-readable storage medium such as an internal
disk (not shown) or a removable disk.
[0021] The present disclosure provides a method and system for vehicle speed profile
generation.
[0022] Referring to Figure 1, it is a flow chart illustrating method for vehicle speed
profile generation.
[0023] The process starts at step 102, data pertaining to driver characteristics and
characteristics of trips taken by said driver is received. At step 104, driver profile is created by
generating skill and aggression parameters for said driver. At step 106, trip parameters
pertaining to said trips taken by the driver by processing the skill and aggression parameters
are constructed. At step 108, acceleration dataset for said trips are constructed. At step 110,
speed values are constructed from the acceleration dataset and at step 112, the speed values
are processed for anomalies.
[0024] Referring to Figure 2, it is a block diagram of a system for vehicle speed
profile generation. The system comprises of a data reception module (202) adapted for
receiving data pertaining to driver characteristics and characteristics of trips taken by said
driver, a driver profile generator module (204) adapted for creating driver profile by
generating skill and aggression parameters for said driver, a trip identifier module (206)
adapted for constructing trip parameters pertaining to said trips taken by the driver by
processing the skill and aggression parameters, an acceleration generator module (208)
adapted for constructing acceleration dataset for said trips, a speed generator module (210)
adapted for constructing speed values from the acceleration dataset and a validation module
(212) adapted for processing the speed values for anomalies.
[0025] In an embodiment of the present invention, the data pertaining to driver
characteristics and characteristics of trips taken by said driver are selected from a group
comprising of type of driver, average trip length, number of trips per driver, trip geographical
details, vehicle type and road type.
[0026] In another embodiment of the present invention, the type of driver is selected
from a group comprising of novice, cautious, risky and rival; the trip geographical details are
obtained from a group comprising of climate, traffic situation and time; the vehicle type is
selected from a group comprising of small car, sedan and SUV, wherein the vehicle type is
selected as sedan by default; the road type is selected from a group comprising of very bad,
bad, average, good and very good.
[0027] In another embodiment of the present invention, a trip is a continuous journey
segment of duration T and location is used to generate data specific to that locations driving
pattern. Typically a city (e.g. New York) is location. Driving data is location specific and
depends on locality.
[0028] In another embodiment of the present invention, in the driver profile generator module (204), driver behavior modeling is specific to a peer group and not absolute. For a group of driver and each driver having multitude of trips his behavior is identified as follows.
i. For each completed trip kurtosis, the normalized fourth order moment
about mean of longitudinal acceleration, is computed.
ii. Now for each driver there is a collection of kurtosis values. Number of
kurtosis values for a driver is equal to the number of trips taken by him.
iii. For each driver u and p are computed, wherein
u = standard deviation of kurtosis values of longitudinal acceleration for
a driver
p = mean of kurtosis values of longitudinal acceleration for a driver
Skill Score Absolute (SSA)= l/a
Aggression Score Absolute (ASA)= p
iv. For each driver there is a SSA and ASA. Using these values we
compute
Local Skill (LS) Coefficients and Local Aggression (LA) Coefficients.
LS Coefficient= mean of SSA, standard deviation of SSA
LA Coefficient= mean of ASA, standard deviation of ASA
A table for these values which is leamt from real data is kept in database for
each location types.
v. For each driver Relative Skill Score (RSS) and Relative Aggression
Score (RAS) are computed as follows
RSS= SSA -mean of SSA standard deviation of SSA
RAS= RAS- mean of RAS standard deviation of RAS
[0029] In an exemplary embodiment of the present invention, In a group of drivers as shown in a scatter plot in accordance with figure 1, it gives a visual representation of skill and aggression. High skill score (RSS) means driver have more skill and high aggression score
(RAS) means driver is more aggressive. Since the data is normalized the plot is centered on
origin (0, 0) in a 2-D plane, referred as skill-aggression plane. Out of all data points 50% of
entire data (i.e. no. of drivers) centered around origin are categorized as normal. A circle with
origin as center is drawn so that 50% of all drivers fall inside the circle. The radius of the
drawn circle varies based on geography. Those radii are stored for all geography. Remaining
drivers (points) are classified as follows:
a) Novice: low skill and low aggression.
b) Cautious: high skill and low aggression
c) Risky: low skill and high aggression
d) Rival: high skill and high aggression
Based on Skill and Aggression 5 driver type exists. The data reception module (202)
comes with number of drivers, number of trips per driver, driver type etc. For a specified
driver type a point is selected in the corresponding region in skill-aggression plane. The radius
of circle, LS Coefficient and LA Coefficient are taken from database based on specified
geography. Then for each driver there is a specific value of mean and standard deviation of
kurtosis. Normal random numbers are generated and each number corresponds to a trip. After
that for each trip a collection of acceleration using Pearson type VII distribution is generated.
Kurtosis, geography and driver type are taken as input. Next, the speed generator module
(210) creates speed profile for each set of acceleration.
[0030] In another embodiment of the present invention, the driver profile generator
module (204) has a Skill and Aggression quantifier which generates a normalized skill and
aggression value set for each driver. For example let a driver is of type normal. Then for this
driver a skill and aggregation parameters are generated. Generation of these two numbers for each driver is done by this module. These values are normalized values. After this step each driver has a pair of mean and standard deviation of kurtosis values.
[0031] In another embodiment of the present invention, in the trip identifier module
(206), two functionalities take place- parameter denormalization and trip level parameter
construction. For parameter denormalization, mean and standard deviation of kurtosis value
for each driver is used to generate an actual (i.e. denormalized) value of mean and standard
deviation (GD, FD). These values are denormalized by using 'geography' parameter from data
reception module (202).
GD= G *CGeo
pD =* CGeo
where CGeo is the geography specific parameter obtained from database.
For trip level parameter construction, generated pairs of (pD, GD ) for all drivers are taken and
N normal random variables N(D, aD) for each pair are generated. After this step N kurtosis
values per driver are accumulated.
[0032] In another embodiment of the present invention, in the acceleration generator
module (208), for M driver and N trips per driver, there will be MN values of kurtosis. For
each driver N values of kurtosis and driving type is present. Based on this, the acceleration
generator module (208) generates N set of acceleration values which follows Pearson type VII
distribution. Geography and driver type is used to get input parameters for underlying Pearson
type VII process. For each driver acceleration values are validated. Acceleration profile is
compared with expected acceleration profile (i.e. probability density) of particular driver type.
If they match acceleration profile is saved as a map with corresponding kurtosis value as key,
where key is the map-pair.
[0033] In another embodiment of the present invention, in the speed generator module
(210), set of acceleration values comes as input and speed time series data is generated as output. At first all acceleration values are portioned into 5 categories.
1. Normal(-1.2 m/s^2 < value<1.2m/sA2)
2. High (2.77 m/sA2 >= value >= 1.2m/sA2)
3. Low ( -2.77 =<value =< -1.2m/sA2)
4. Very high (value >= 2.77m/sA2)
5. Very Low (value =< -2.77m/sA2)
Then these accelerations are stored in memory with their corresponding category.
After that simulation for speed starts. Initially it starts with mode = 'start' as journey is
starting. Once speed reaches 10 m/s mode is updated to 'steady'. There are 5 modes
'start','steady','stop', 'speed up', 'speed down'. At each mode; different no. acceleration
values are taken (based on category given in table 1).
Table 1: Modes and Number of Samples
Mode No of samples (cycle length= Category of acceleration
1)
start Randomly from 8 to 14 Either normal positive or
high.
steady Randomly from 4 to 10 Either positive or negative
normal acceleration.
stop Randomly from 8 to 14 Either low or very low or
negative acceleration
speed up Randomly 2 to 5 Very high
speed down Randomly 2 to 5 Very low
Then from selected acceleration values speed is generated by taking a summation of
acceleration samples. After that mode is updated by logical table 2.
Table 2: Modes and Number of Samples
Mode
(current Next mode
cycle)
if last speed sample >10 m/s
start steady
else
start
Steady Either steady, speed up, speed down
Stop Stop (unless speed =0)
if last speed sample >17 m/s
speed down
speed up else
Steady or speed down
speed down Steady or speed up or start(if last speed sample <.5 m/s)
At any step if speed <0, then speed is set = 0, remaining samples in that cycle are
discarded and mode is changed to start.
[0034] In another embodiment of the present invention, generated speed time series is
validated by the validation module (212) as per geography using boundary conditions for a
drive in the following way:
a) Speed profile: maximum speed, percentage of drive in high speed zone
b) Time: day or night driving
c) Distance (trip length) d) % Speed distribution: based on city or highway drive generated speed is checked for compliance with respect to standard drive cycle models for that geography.
If validation is not successful same set of accelerations are used again to generate
speed values. Once validation is successful, speed values are passed to structured dataset
creation module along with driver type, driver ID. Impact of driving on driver's health is
measured by health index which is a function of speed and acceleration profile as well as
geography, drive condition. For each trip a health index is given in percentage where 1005
means maximum stress level and 0% means no stress is present.
[0035] In another embodiment of the present invention, generated speed values are
stored in a database in cloud or dedicated storage with driver ID, driver type and geography.
For each created journey data this module adds a new journey in database. Thus after each
speed time series creation generated speed data is stored in a structured database for
corresponding driver. Once all the required data are created the link to updated generated data
is given as output. From the link all or part of data as can be obtained as per requirement.
[0036] In an exemplary embodiment of the present invention, data as per figure 3 is
stored in structured database and made available to a user.
[0037] The written description describes the subject matter herein to enable any
person skilled in the art to make and use the embodiments. The scope of the subject matter
embodiments is defined by the claims and may include other modifications that occur to those
skilled in the art. Such other modifications are intended to be within the scope of the claims if
they have similar elements that do not differ from the literal language of the claims or if they
include equivalent elements with insubstantial differences from the literal language of the
claims.
[0038] It is, however to be understood that the scope of the protection is extended to
such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g.
hardware means like e.g. an application-specific integrated circuit (ASIC), a field
programmable gate array (FPGA), or a combination of hardware and software means, e.g. an
ASIC and an FPGA, or at least one microprocessor and at least one memory with software
modules located therein. Thus, the means can include both hardware means and software
means. The method embodiments described herein could be implemented in hardware and
software. The device may also include software means. Alternatively, the embodiments may
be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0039] The embodiments herein can comprise hardware and software elements. The
embodiments that are implemented in software include but are not limited to, firmware,
resident software, microcode, etc. The functions performed by various modules described
herein may be implemented in other modules or combinations of other modules. For the
purposes of this description, a computer-usable or computer readable medium can be any
apparatus that can comprise, store, communicate, propagate, or transport the program for use
by or in connection with the instruction execution system, apparatus, or device.
[0040] The medium can be an electronic, magnetic, optical, electromagnetic, infrared,
or semiconductor system (or apparatus or device) or a propagation medium. Examples of a
computer-readable medium include a semiconductor or solid state memory, magnetic tape, a
removable computer diskette, a random access memory (RAM), a read-only memory (ROM),
a rigid magnetic disk and an optical disk. Current examples of optical disks include compact
disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W), BLU-RAY, and
[0041] A data processing system suitable for storing and/or executing program code
will include at least one processor coupled directly or indirectly to memory elements through
a system bus. The memory elements can include local memory employed during actual
execution of the program code, bulk storage, and cache memories which provide temporary
storage of at least some program code in order to reduce the number of times code must be
retrieved from bulk storage during execution.
[0042] Input/output (I/O) devices (including but not limited to keyboards, displays,
pointing devices, etc.) can be coupled to the system either directly or through intervening I/O
controllers. Network adapters may also be coupled to the system to enable the data processing
system to become coupled to other data processing systems or remote printers or storage
devices through intervening private or public networks. Modems, cable modem and Ethernet
cards are just a few of the currently available types of network adapters.
[0043] A representative hardware environment for practicing the embodiments may
include a hardware configuration of an information handling/computer system in accordance
with the embodiments herein. The system herein comprises at least one processor or central
processing unit (CPU). The CPUs are interconnected via system bus to various devices such
as a random access memory (RAM), read-only memory (ROM), and an input/output (I/O)
adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives,
or other program storage devices that are readable by the system. The system can read the
inventive instructions on the program storage devices and follow these instructions to execute
the methodology of the embodiments herein.
[0044] The system further includes a user interface adapter that connects a keyboard,
mouse, speaker, microphone, and/or other user interface devices such as a touch screen device
(not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
[0045] The preceding description has been presented with reference to various
embodiments. Users having ordinary skill in the art and technology to which this application
pertains will appreciate that alterations and changes in the described structures and methods of
operation can be practiced without meaningfully departing from the principle, spirit and
scope.
[0046] The reference to any prior art in this specification is not, and should not be
taken as, an acknowledgement or any form of suggestion that the prior art forms part of the
common general knowledge in Australia.
[0047] In this specification, the terms "comprise", "comprises", "comprising" or
similar terms are intended to mean a non-exclusive inclusion, such that a system, method or
apparatus that comprises a list of elements does not include those elements solely, but may
well include other elements not listed.
[0048] This application is a divisional application from Australian application 2019201484.
The full disclosure of AU2019201484 is incorporated herein by reference.
Claims (11)
1. A method for vehicle speed profile generation, said method comprising processor implemented steps of:
receiving datapertaining to driver characteristics and characteristics of trips taken by said driver;
creating driver profile by generating skill parameter and aggression parameter for said driver, wherein the skill parameter is a standard deviation of kurtosis values of longitudinal acceleration for all the trips taken by said driver and the aggression parameter is a mean of the kurtosis values of longitudinal acceleration for all the trips taken by said driver;
constructing trip parameters pertaining to said trips taken by the driver by processing the skill parameter and aggression parameter;
constructing an acceleration dataset for said trips;
constructing speed values from the acceleration dataset; and
processing the speed values for anomalies using a validation module.
2. The method as claimed in claim 1, wherein the data pertaining to driver characteristics and characteristics of trips taken by said driver are selected from a group comprising: type of driver, average trip length, number of trips per driver, trip geographical details, vehicle type and road type.
3. The method as claimed in claim 2, wherein the type of driver is selected from a group comprising of novice, cautious, risky and rival.
4. The method as claimed in claim 2, wherein the trip geographical details are selected from a group comprising of climate, traffic situation and time.
5. The method as claimed in claim 2, wherein the vehicle type is selected from a group comprising of small car, sedan and SUV.
6. The method as claimed in claim 5, wherein the vehicle type is selected as sedan by default.
7. The method as claimed in any one of claims 1 to 6, wherein the speed values are processed for anomalies and subsequent validation before selection of said speed values.
8. The method as claimed in claim 7, wherein the speed values are reprocessed if validation of said speed values result in failure.
9. A system for vehicle speed profile generation, said system comprising: a processor;
a data bus coupled to said processor; and a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured to:
receive data pertaining to driver characteristics and characteristics of trips taken by said driver;
create a driver profile by generating skill parameter and aggression parameter for said driver, wherein the skill parameter is a standard deviation of kurtosis values of longitudinal acceleration for all the trips taken by said driver and the aggression parameter is a mean of the kurtosis values of longitudinal acceleration for all the trips taken by said driver;
construct trip parameters pertaining to said trips taken by the driver by processing the skill parameter and aggression parameter;
construct an acceleration dataset for said trips; construct speed values from the acceleration dataset; and process the speed values for anomalies.
10. One or more non-transitory machine readable information storage media comprising one or more instructions which when executed by one or more hardware processors causes the one or more hardware processor to perform a method for vehicle speed profile generation, said method comprising: receiving data pertaining to driver characteristics and characteristics of trips taken by said driver using a data reception module;
creating a driver profile by generating skill parameter and aggression parameter for said driver using a driver profile generator module, wherein the skill parameter is a standard deviation of kurtosis values of longitudinal acceleration for all the trips taken by said driver and the aggression parameter is a mean of the kurtosis values of longitudinal acceleration for all the trips taken by said driver;
constructing trip parameters pertaining to said trips taken by the driver by processing the skill parameter and aggression parameter using a trip identifier module;
constructing an acceleration dataset for said trips using an acceleration generator module;
constructing speed values from the acceleration dataset using a speed generator module; and
processing the speed values for anomalies using a validation module.
11. The one or more non-transitory machine readable information storage media of claim 10, wherein the type of driver is selected from a group comprising of novice, cautious, risky and rival.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AU2021202991A AU2021202991B2 (en) | 2016-07-15 | 2021-05-11 | Method and system for vehicle speed profile generation |
Applications Claiming Priority (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
IN201621024234 | 2016-07-15 | ||
IN201621024234 | 2016-07-15 | ||
AU2017205114A AU2017205114A1 (en) | 2016-07-15 | 2017-07-17 | Method and system for vehicle speed profile generation |
AU2019201484A AU2019201484A1 (en) | 2016-07-15 | 2019-03-04 | Method and system for vehicle speed profile generation |
AU2021202991A AU2021202991B2 (en) | 2016-07-15 | 2021-05-11 | Method and system for vehicle speed profile generation |
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AU2019201484A Division AU2019201484A1 (en) | 2016-07-15 | 2019-03-04 | Method and system for vehicle speed profile generation |
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AU2021202991A1 AU2021202991A1 (en) | 2021-06-17 |
AU2021202991B2 true AU2021202991B2 (en) | 2023-02-23 |
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AU2021202991A Active AU2021202991B2 (en) | 2016-07-15 | 2021-05-11 | Method and system for vehicle speed profile generation |
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AU2019201484A Abandoned AU2019201484A1 (en) | 2016-07-15 | 2019-03-04 | Method and system for vehicle speed profile generation |
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EP (1) | EP3272612B1 (en) |
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CN109859512A (en) * | 2019-02-12 | 2019-06-07 | 桂林电子科技大学 | A kind of typical curved areas single car dynamic guiding method under V2X environment |
CN113689594A (en) * | 2021-08-10 | 2021-11-23 | 南通大学 | Novel automobile driving condition construction method |
CN113968231B (en) * | 2021-12-09 | 2022-08-09 | 吉林大学 | Intelligent driver model parameter determination method conforming to driver habits |
CN114566046A (en) * | 2022-03-01 | 2022-05-31 | 海南大学 | Short-time traffic condition prediction system and method thereof |
CN114577232A (en) * | 2022-03-08 | 2022-06-03 | 广东皓行科技有限公司 | Automatic vehicle navigation method and device with lost differential signal |
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EP1529695A1 (en) * | 2003-10-14 | 2005-05-11 | Delphi Technologies, Inc. | Driver adaptive collision warning system |
US20110267185A1 (en) * | 2010-04-30 | 2011-11-03 | General Electric Company | Vehicle and driver monitoring system and method thereof |
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CN101228546A (en) * | 2005-06-01 | 2008-07-23 | 茵诺苏伦斯公司 | Motor vehicle traveling data collection and analysis |
EP2426648A1 (en) | 2010-09-01 | 2012-03-07 | Key Driving Competences | A driver behavior diagnostic method and system |
US8634822B2 (en) | 2012-06-24 | 2014-01-21 | Tango Networks, Inc. | Automatic identification of a vehicle driver based on driving behavior |
EP2909157A1 (en) | 2012-10-16 | 2015-08-26 | Ims Solutions, Inc. | Driving event classification system |
US10083613B2 (en) * | 2013-01-06 | 2018-09-25 | Ionroad Technologies Ltd. | Driving support |
IN2014MU00871A (en) * | 2014-03-14 | 2015-09-25 | Tata Consultancy Services Ltd |
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- 2017-07-14 EP EP17181422.1A patent/EP3272612B1/en active Active
- 2017-07-17 AU AU2017205114A patent/AU2017205114A1/en not_active Abandoned
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Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
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EP1529695A1 (en) * | 2003-10-14 | 2005-05-11 | Delphi Technologies, Inc. | Driver adaptive collision warning system |
US20110267185A1 (en) * | 2010-04-30 | 2011-11-03 | General Electric Company | Vehicle and driver monitoring system and method thereof |
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US10571290B2 (en) | 2020-02-25 |
AU2021202991A1 (en) | 2021-06-17 |
EP3272612A1 (en) | 2018-01-24 |
AU2017205114A1 (en) | 2018-02-01 |
EP3272612B1 (en) | 2021-10-20 |
AU2019201484A1 (en) | 2019-03-28 |
US20180017402A1 (en) | 2018-01-18 |
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